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7th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2022

作者： Das, Mousumi Choudhary, Tilendra Bhuyan, M.K. Sharma, L.N. Pallab Jyoti Dutta, H. Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Department of Electronics and Electrical Engineering Assam Guwahati India

ISBN: (纸本)9781665496483

In recent years, camera-based non-contact heart rate (HR) measurement technology has grown immensely. The system captures the reflection of light from the facial tissues and lead to the formation of a remote photoplethysmogram (rPPG) signal that can be used to measure physiological parameters for cardiac health assessment. Due to environmental interferences, extraction of a reliable rPPG signal is a challenging task and thus, requires a robust denoising algorithm. In this paper, a discrete wavelet transform (DWT)-based multiresolution method is used to remove the noises from the video frames caused due to illumination variation and motion artifacts. Subsequently, rPPG signal is extracted and HR is measured from two region of interests (ROIs), facial and forehead regions. The study evaluates the performance of the proposed method on each of the RGB color channels from both the ROIs. The performance results for the COHFACE dataset show that the proposed method works well for the estimation of HR values. Furthermore, they reveal that the forehead region on the green channel is more suitable for HR measurement. © 2022 IEEE.

关键词： Discrete wavelet transforms

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The Colony Predation Algorithm

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Journal of Bionic engineering 2021年第3期18卷 674-710页

作者： Jiaze Tu Huiling Chen Mingjing Wang Amir H.Gandomi College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Faculty of Engineering and Information Technology University of Technology SydneyUltimoNSW 2007Australia

This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in *** utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another ***,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment *** paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation *** proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering *** results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other ***,the source code of the CPA will be publicly available after publication.

关键词： Colony Predation Algorithm optimization nature-inspired computing meta-heuristic engineering problems

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A Hybrid Heuristic Algorithm for Solving COVID-19’s Social Distancing at Universities Campus

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Computer Systems science & engineering 2022年第6期41卷 933-944页

作者： Hassan Al-Tarawneh Khalid Al-Kaabneh Aysh Alhroob Hazem Migdady Issam Alhadid Department of Data sciences and Artificial Intelligence Al-Ahlyyia Amman UniversityAmmanJordan Department of Software Engineering Isra UniversityAmmanJordan Department of CSMIS Oman college of management and technologyMuscatOman Faculty of Information Technology and Systems University of JordanAqabaJordan

Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main ***, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.

关键词： COVID-19 social distance variable neighborhood search adaptive neighborhood structure university course timetable

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Maximizing Crop Yields and Water Efficiency through Tailored Irrigation System Design and Crop Selection

Maximizing Crop Yields and Water Efficiency through Tailored...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Musale, Prajakta Jadhav, Balasaheb Bahir, Suvarna A Thopate, Kaushalya Anarase, Dipali Jaiswal, Krishna Vishwakarma Institute of Technology Department of Engineering Sciences and Humanities Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Trinity Academy of Engineering Department of Computer Engineering India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Wipro Technology India

ISBN: (纸本)9798350308167

Today various water-pump operating systems are available in the market, which allows the user/farmer to operate his water-pump from a distant location using mobile app or on a phone call to the system which is integrated with the water-pump. While studying the existing system we came across a problem that, existing systems does not provide any solution for optimizing the usage of water and power. Optimizing the use of power and water in farming has become a real-time problem these days. To overcome this problem, we have proposed an IOT based system "Advanced Irrigation System". This system provides automation for operating the water pump in the farm by automatically turning ON and OFF the water-pump depending on the type of the crop and its moisture requirements. Also, proposed system provides farmer interface via a mobile application in his regional language, where he can check the moisture, humidity and temperature of his farm. Along with this proposed system has a Google Assistant integrated, using which famer can operate the water-pump remotely over a voice command. © 2023 IEEE.

关键词： Advanced Irrigation System Crop Selection Maximizing Crop Yields power Optimization Water Efficiency water-pump operating systems

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Blockchain Oriented Hybrid Architecture for Crowdsource Model

Blockchain Oriented Hybrid Architecture for Crowdsource Mode...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Pachhaiammal Alias Priya, M. Rajulu, N. Sundara Jayanthi, S. Kavitha, G. Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Excel Engineering College Department of Information Technology Tamil Nadu Coimbatore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798350333602

Blockchain has been preserved as one of the strongest innovations to facilitate crowdsourcing as it offers new functionality, such as decentralization and transparency. Alas, certain intrinsic drawbacks of Blockchain, which become the efficiency bottleneck of crowdsourcing schemes, have hardly been tackled by the most current initiatives when implementing Blockchain to clustering. The followings are the few challenges faced by Blockchain: Scalability, the blockchain sector still face difficulties in managing a large number of users at once, Hackers and shadow dealing, Complex to understanding and adopting. In this article, to achieve decentralization and privacy protection, a modern hybrid blockchain crowd sourcing facility is proposed. With its hybrid Blockchain, doubleheaders, and double consensus algorithms, this network integrates stable connectivity between the requestors and the employees. In order to ensure the automated execution of the activities and the security of users' privacy, smart contracts and zero awareness evidence are both used. Finally, tests are performed to validate the effectiveness of the platform's consensus algorithm by matching it with the new one. The proposed hybrid framework proved that it is highly confidential and gives an extraordinary performance in regard to privacy. With the development of big data and medical information control systems, there is a growing interest in the sharing of electronic medical records between businesses for better medical care and advancement. On the other hand, the proliferation of large amounts of data, privacy issues, inherent business confidence issues, and complex legal requirements impede the quick development of healthcare intelligence. © 2023 IEEE.

关键词： Blockchain

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A Review of Machine Learning and Deep Learning algorithms for Chronic Kidney Disease Prediction 2

A Review of Machine Learning and Deep Learning algorithms fo...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Nagre, Pratik Verma, Prateek Kumar, Praveen Gavankar, Nitin L. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Maharashtra Wardha442001 India Walchand College of Engineering Department of Computer Science and Engineering Maharashtra Sangli416415 India

ISBN: (纸本)9798331528713

Chronic Kidney Disease (CKD) is a common disease worldwide that is manifested by slow progressive loss of kidney function that can result in complications and kidney failure. That is why conventional approaches to CKD diagnosis and treatment have certain difficulties: late identification of the disease, inaccuracy of the diagnosis, and high costs for the therapy. The new approaches for solving these problems are opened with the help of Machine Learning (ML) and deep learning (DL). These technologies can help existing information sets in large volumes in diagnosing, predicting, and in early detection of diseases to enhance the well-being of the patients. However, the outcome comes with a handful of constraints, including the quality of the data used in developing the models, the ability to interpret results from the generated model, and the complexity of the computation process. The following section describes the various ML and DL algorithms used for CKD, their strengths, weaknesses, and the possibility of implementing these technologies in clinical settings for better CKD outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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Income Tax Fraud Deduction Using Machine Learning 4

Income Tax Fraud Deduction Using Machine Learning

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sudhakaran, Pradeep Kolamudi, Prudhvi Preetha, M. Pragash, K. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India Srm Institute of Science and Technology Career Development Centre Potheri Tamil Nadu Kattankulathur603203 India Sri Manakula Vinayagar Engineering College Department of Artificial Intelligence and Data Science Madagadipet Puducherry605107 India

ISBN: (纸本)9798350364729

Specifically, the program is based on boosting methods in the context of the used machine learning, to reveal potential cases of evasion of income tax. The procedure consists of obtaining and using datasets of income tax for the learning and training purpose. The first and crucial goal is to enhance the accuracy of predicting fraud cases through learning boosting algorithms and graphical representation of the most significant factors leading to fraud detection. Depending on accuracy and sensitivity as well as specificity and precision for that matter, the said assessment will be conducted. The first goal entails a mechanization of the process of identifying foraging activities, eliminating possible errors, and improving efficiency of and security in income tax assessments with particular emphasis in distinguishing between probable real and potentially fraudulent tax returns [10]. The boosting techniques alongside other futuristic ways of identifying the suspicious transactions can help the tax authorities to improve the risk management processes of their organization. In other words, this approach of fraud detection makes it possible to proceed to more efficient work in terms of both accuracy and the reliability of certain results. Also, the channels of tax avoidance can be closed by concentrating efforts on cases of high risk as the authorities are more likely to lose a lot of revenues. Machine learning technologies can easily address these requirements, giving the necessary flexibility and precision to prevent and identify fraudulent activities and strategies. These strategies are critical in protecting the Tax system a longer term game which can effectively counter financial fraudsters. The goal of the project are to gather information on the corporate taxation;to apply boosting algorithms for fraudulent activity detection, and to evaluate the efficiency of boosting algorithms. The main focus is primarily aimed at the increased application of automatic extraction

关键词： Risk management

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Overview of the Second Shared Task on Fake News Detection in Dravidian Languages: DravidianLangTech@EACL 2024 4

Overview of the Second Shared Task on Fake News Detection in...

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4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024

作者： Subramanian, Malliga Chakravarthi, Bharathi Raja Shanmugavadivel, Kogilavani Pandiyan, Santhiya Kumaresan, Prasanna Kumar Palani, Balasubramanian Premjith, B. Vanaja Mithunajha, S. Devika, K. Hariprasath, S.B. Haripriya, B. Vigneshwar, E. Kongu Engineering College Tamil Nadu India School of Computer Science University of Galway Ireland Data Science Institute University of Galway Ireland National Institute of Technology Tamil Nadu India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798891760783

The rise of online social media has revolutionized communication, offering users a convenient way to share information and stay updated on current events. However, this surge in connectivity has also led to the proliferation of misinformation, commonly known as fake news. This misleading content, often disguised as legitimate news, poses a significant challenge as it can distort public perception and erode trust in reliable sources. This shared task consists of two subtasks such as task 1 and task 2. Task 1 aims to classify a given social media text into original or fake. The goal of the FakeDetect-Malayalam task2 is to encourage participants to develop effective models capable of accurately detecting and classifying fake news articles in the Malayalam language into different categories like False, Half True, Mostly False, Partly False, and Mostly True. For this shared task, 33 participants submitted their results. © 2024 Association for Computational Linguistics.

关键词： Fake detection

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A Novel Approach for Anomaly Detection using Snort Integrated with Machine Learning

A Novel Approach for Anomaly Detection using Snort Integrate...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T. Preethi Ponnuru Rakshitha Reddy Lekkala Likhitha Pendyala Pavan Kumar Abhinav Kamani Department of CSE – Cyber Security Data Science and Artificial Intelligence & Data Science VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

In today’s digital world, it is crucial to keep a company’s information safe from cyber threats. With new and more sophisticated network attacks emerging all the time, better security measures and ways to monitor our networks are needed. Intrusion Detection Systems (IDS) can act as digital guards to help protect our networks from these malicious threats. Snort is a widely used open-source IDS, but it can struggle with the ever-changing nature of these threats. Our research focuses on enhancing Snort’s capabilities by integrating it with ML algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes. We also conducted subset testing using the NSL-KDD dataset. Our results indicate that combining these ML algorithms with Snort can improve our ability to detect and stop malicious network activities. This research can be helpful for anyone interested in developing smarter and more effective intrusion detection systems.

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A review on generative based methods for MRI reconstruction

A review on generative based methods for MRI reconstruction

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2022 International Symposium on Intelligent Unmanned Systems and artificial intelligence, SIUSAI 2022

作者： Zhao, Xiang Yang, Tiejun Li, Bingjie School of Information Science and Engineering Henan University of Technology Zhengzhou China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China

Magnetic resonance imaging (MRI) is one of the most important methods for clinical diagnosis. However, the main drawback of MRI is the long imaging time, which will cause the moving artifact by patient movements. With the rapid development of the computing power of computer, deep learning is widely used in computer vision, natural language processing, visual recognition and so on. Meanwhile, a large number of reconstruction methods based on deep learning have also emerged. Recently, many generative models have been proposed to solve the perception quality problem that existed in fast MRI images. In this paper, we manage to survey the motivations and reconstruction strategies of generative-based methods published in journals and conferences over the past five years. First, the background and theoretical basis of MRI reconstruction are introduced. Secondly, the application of generative-based methods in MRI reconstruction field is comprehensively summarized and analyzed, including Generative Adversarial Network (GAN), Variational Autoencoder (VAE) and VAE-GAN. Then the advantages and disadvantages of the existing generative-based MRI reconstruction methods are discussed. Finally, several publicly available MR image datasets and evaluation metrics are presented, which can provide a reference for researchers and practitioners working in related domains. The conclusions and challenges are also given. © Published under licence by IOP Publishing Ltd.

关键词： Magnetic resonance imaging

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